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In this paper, the system identification theory and method using dynamic neural networks are presented, the multilayer feedforward networks employed, the backpropagation with adaptive learning rate algorithms proposed. Finally the comparision of network output with that of the hydrostatic drive system of secondary regulation is given, and output error, sum-squared error et al, or the results that embody the effect of system identification given sine input to it are provided.

In this paper the working principle of hydraulic branch of the hydromechanical transmission is introduced, dynamic characteristic analysed, its actuating medium enclosed in the working space consists that of pump, soft pipeline and motor considered to be a torsional hydraulic spring whose torsional stiffness and damping provided. The multidegrees-of-freedom of the concentrated mass-elasticity discrete mechanics model is utilized to analyze the transmission system, and the finite element method is employed to model it, the torsional natural characteristic of the system are calculated, these provide the scientific basis for structural modification of controlling the vibration and noise.

In this paper the nonlinear system identification theory and method using neural networks are presented, the multilayer feedforward networks employed, the backpropagation learning algorithm proposed. The inputs of the networks are consisted of angular velocity and throttle angle, and outputs torque of the engine, finally the comparision of simulation result with that of experiment and other results that embody the effect of system identification are given. Relative studies revealed that the nonlinear system identification for the engine of automated automobiles using neural networks can be effective.

In this paper, the intelligent control theory and method using neural networks are presented, the multilayer feedforward networks employed, the back propagation learning algorithm proposed. Finally the angular velocity control result of the hydrostatic drive system of secondary regulation with such type of control is provided. Relative studies revealed that the intelligent control for the system using neural networks can be effective.